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Record W4308713366 · doi:10.24908/pceea.vi.15974

Persistent mistakes in learning basic circuit analysis

2022· article· en· W4308713366 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsConcordia University
Fundersnot available
KeywordsNetwork analysisMathematics educationComputer scienceElectronicsElectrical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Basic circuit analysis is a core course in most of the undergraduate engineering programs and is the prerequisite course for many other courses in the undergraduate electrical engineering program. Students enter into engineering schools with varying knowledge of the concepts of basic circuit analysis depending on whether they come from high school, CEGEP, or a technical college, etc. Many students from all engineering majors struggle to learn the concepts taught in these courses which creates challenges for both faculty members and students in courses when for which basic circuit analysis is a pre-requisite course. There is more research done in understanding the conceptual knowledge of physics of electricity and electric (and electronic) components and improving the instruction of basic circuit analysis concepts, but not enough work is done to understand the mistakes undergraduate electrical engineering students continue to make course after course. For this study, the authors look at the persistent problems in learning circuit analysis techniques by looking at students’ use of these techniques in three core courses in electrical engineering program namely electronics 1, electronics 2 and electromagnetic waves and guiding structures. Students’ responses to exam questions that specifically expected students to use these concepts are analyzed. The objective of the study was to analyze whether the understanding of the application of circuit analysis techniques get better as students continue to use these concepts in more courses and applications, or the problems persist. Results show that the students persistently make mistakes in applying KVL and KCL equations, nodal analysis, superposition theorem, voltage divider, and mesh analysis. Additionally, the results reveal that students persistently make mistakes in questions that involve the concepts of load and no load, open circuit, series components, parallel components, voltage drop across the current source, and voltage gain. It is noted that the mistakes made by students do not get much better as they continue taking more courses. The results of this study are important from many aspects. They are helpful to understand the continuing struggles of students and so are helpful to design pedagogy and assessment in a way that these concepts can be well explained. Thorough understanding of the concepts in a course that is as important as basic circuit analysis is important to achieve many engineering education goals including student retention, motivation, innovation, and inclusion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.176
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it